| | --- |
| | license: cc-by-nc-4.0 |
| | task_categories: |
| | - image-to-3d |
| | library_name: |
| | - open3d |
| | --- |
| | |
| | # MaizeField3D: A Curated 3D Point Cloud Dataset of Field-Grown Plants From A Maize Diversity Panel |
| |
|
| | [Paper link](https://huggingface.co/papers/2503.07813) | [Project page](https://baskargroup.github.io/MaizeField3D/) |
| |
|
| | ## Overview |
| | The use of artificial intelligence (AI) in three-dimensional (3D) agricultural research, especially for maize, |
| | has been limited due to the lack of large-scale, diverse datasets. While 2D image datasets are widely available, |
| | they fail to capture key structural details like leaf architecture, plant volume, and spatial arrangements—information |
| | that 3D data can provide. To fill this gap, we present a carefully curated dataset of 3D point clouds representing fully |
| | field-grown maize plants with diverse genetic backgrounds. This dataset is designed to be AI-ready, offering valuable |
| | insights for advancing agricultural research. |
| |
|
| | Our dataset includes over 1,000 high-quality point clouds of maize plants, collected using a Terrestrial Laser Scanner. |
| | These point clouds encompass various maize varieties, providing a comprehensive and diverse dataset. To enhance usability, |
| | we applied graph-based segmentation to isolate individual leaves and stalks. Each leaf is consistently color-labeled based |
| | on its position in the plant (e.g., all first leaves share the same color, all second leaves share another color, and so on). |
| | Similarly, all stalks are assigned a unique, distinct color. |
| |
|
| | A rigorous quality control process was applied to manually correct any segmentation or leaf-ordering errors, ensuring |
| | accurate segmentation and consistent labeling. This process facilitates precise leaf counting and structural analysis. |
| | In addition, the dataset includes metadata describing point cloud quality, leaf count, and the presence of tassels and maize cobs. |
| |
|
| | To support a wide range of AI applications, we also provide code that allows users to sub-sample the point clouds, |
| | creating versions with user-defined resolutions (e.g., 100k, 50k, 10k points) through uniform downsampling. |
| | Every version of the dataset has been manually quality-checked to preserve plant topology and structure. |
| | This dataset sets the stage for leveraging 3D data in advanced agricultural research, particularly for maize phenotyping and plant structure studies. |
| |
|
| | ## Dataset Directory Structure |
| |
|
| | ``` |
| | MaizeField3D/ |
| | ├── MaizeField3d/ # Main Python package directory |
| | │ ├── __init__.py # Initialize the Python package |
| | │ ├── dataset.py # Python file to define dataset access functions |
| | ├── setup.py # Package setup configuration |
| | ├── README.md # Package description |
| | ├── requirements.txt # Dependencies |
| | ├── MANIFEST.in # Non-Python files to include in the package |
| | ├── Metadata.xlsx # Metadata for your dataset |
| | ├── PointCloudDownsampler.py # Python script for downsampling |
| | └── datasets/ # Directory for zipped datasets |
| | ├── FielGrwon_ZeaMays_RawPCD_100k.zip |
| | ├── FielGrwon_ZeaMays_RawPCD_50k.zip |
| | ├── FielGrwon_ZeaMays_RawPCD_10k.zip |
| | ├── FielGrwon_ZeaMays_SegmentedPCD_100k.zip |
| | ├── FielGrwon_ZeaMays_SegmentedPCD_50k.zip |
| | ├── FielGrwon_ZeaMays_SegmentedPCD_10k.zip |
| | ├── FielGrwon_ZeaMays_Reconstructed_Surface_dat.zip |
| | ├── FielGrwon_ZeaMays_Reconstructed_Surface_stl.zip |
| | ``` |
| |
|
| | ### Contents of the `.zip` Files |
| | - **`FielGrwon_ZeaMays_RawPCD_100k.zip`**: |
| | - Contains 1045 `.ply` files. Each file has 100K point cloud representing an entire maize plant. |
| | |
| | - **`FielGrwon_ZeaMays_RawPCD_50k.zip`**: |
| | - Contains 1045 `.ply` files. Each file has 50K point cloud representing an entire maize plant. |
| |
|
| | - **`FielGrwon_ZeaMays_RawPCD_10k.zip`**: |
| | - Contains 1045 `.ply` files. Each file has 10K point cloud representing an entire maize plant. |
| | |
| | - **`FielGrwon_ZeaMays_SegmentedPCD_100k.zip`**: |
| | - Contains 520 `.ply` files. Each file represents a segmented maize plant by 100K point cloud focusing on specific plant parts. |
| |
|
| | - **`FielGrwon_ZeaMays_SegmentedPCD_50k.zip`**: |
| | - Contains 520 `.ply` files. Each file represents a segmented maize plant by 50K point cloud focusing on specific plant parts. |
| | |
| | - **`FielGrwon_ZeaMays_SegmentedPCD_10k.zip`**: |
| | - Contains 520 `.ply` files. Each file represents a segmented maize plant by 10K point cloud focusing on specific plant parts. |
| |
|
| | - **`FielGrwon_ZeaMays_Reconstructed_Surface_stl.zip`**: |
| | - Contains 520 `.ply` files. Each file represents the reconstructed surfaces of the maize plant leaves generated from a procedural model. |
| |
|
| | - **`FielGrwon_ZeaMays_Reconstructed_Surface_dat.zip`**: |
| | - Contains 520 `.ply` files. Each file represents the reconstructed NURBS surface information including degree, knot vector, and control point values. |
| |
|
| | **License** |
| | ``` |
| | CC-BY-NC-4.0 |
| | ``` |
| |
|
| | ### How to Access |
| | 1. **Download the `.zip` files**: |
| | - [FielGrwon_ZeaMays_RawPCD_100k.zip](https://huggingface.co/datasets/BGLab/AgriField3D/blob/main/datasets/FielGrwon_ZeaMays_RawPCD_100k.zip) |
| | - [FielGrwon_ZeaMays_RawPCD_50k.zip](https://huggingface.co/datasets/BGLab/AgriField3D/blob/main/datasets/FielGrwon_ZeaMays_RawPCD_50k.zip) |
| | - [FielGrwon_ZeaMays_RawPCD_10k.zip](https://huggingface.co/datasets/BGLab/AgriField3D/blob/main/datasets/FielGrwon_ZeaMays_RawPCD_10k.zip) |
| | - [FielGrwon_ZeaMays_SegmentedPCD_100k.zip](https://huggingface.co/datasets/BGLab/AgriField3D/blob/main/datasets/FielGrwon_ZeaMays_SegmentedPCD_100k.zip) |
| | - [FielGrwon_ZeaMays_SegmentedPCD_50k.zip](https://huggingface.co/datasets/BGLab/AgriField3D/blob/main/datasets/FielGrwon_ZeaMays_SegmentedPCD_50k.zip) |
| | - [FielGrwon_ZeaMays_SegmentedPCD_10k.zip](https://huggingface.co/datasets/BGLab/AgriField3D/blob/main/datasets/FielGrwon_ZeaMays_SegmentedPCD_10k.zip) |
| | 2. **Extract the files**: |
| | ```bash |
| | unzip FielGrwon_ZeaMays_RawPCD_100k.zip |
| | unzip FielGrwon_ZeaMays_RawPCD_50k.zip |
| | unzip FielGrwon_ZeaMays_RawPCD_10k.zip |
| | unzip FielGrwon_ZeaMays_SegmentedPCD_100k.zip |
| | unzip FielGrwon_ZeaMays_SegmentedPCD_50k.zip |
| | unzip FielGrwon_ZeaMays_SegmentedPCD_10k.zip |
| | ``` |
| |
|
| | 3. Use the extracted `.ply` files in tools like: |
| | - MeshLab |
| | - CloudCompare |
| | - Python libraries such as `open3d` or `trimesh`. |
| |
|
| | ### Example Code to Visualize the `.ply` Files in Python |
| | ```python |
| | import open3d as o3d |
| | |
| | # Load and visualize a PLY file from the dataset |
| | pcd = o3d.io.read_point_cloud("FielGrwon_ZeaMays_RawPCD_100k/0001.ply") |
| | o3d.visualization.draw_geometries([pcd]) |
| | ``` |
| |
|
| | **Citation** |
| | If you find this dataset useful in your research, please consider citing our paper as follows: |
| | ``` |
| | @article{kimara2025AgriField3D, |
| | title = "AgriField3D: A Curated 3D Point Cloud Dataset of Field-Grown Plants from a Maize Diversity Panel", |
| | author = "Elvis Kimara, Mozhgan Hadadi, Jackson Godbersen, Aditya Balu, Zaki Jubery, Adarsh Krishnamurthy, Patrick Schnable, Baskar Ganapathysubramanian" |
| | year = "2025" |
| | } |
| | ``` |
